We can generate a new set of images with similar appearance and structure to the image on which the model was trained. This is done by sampling a noisy image \(x_T \sim \mathcal {N}(0,\mathbb {I})\)  and iteratively denoising using our trained model such that \(x_{t-1}=G(x_t)\) . Since our backbone network is fully convolutional, it can be used to generate images of any size by starting from a noisy image with the required size. In fig:imagegenerationresults We show a qualitative comparison to SinGAN [1]} and GPNN [2]}. Note that our results have better quality than SinGAN, similar to GPNN. See project page for more results and a wide qualitative comparison.
